| dc.description.abstract | With the intensification of the global trend of declining birth rates, infant health issues have become particularly important. Premature birth and low birth weight are major factors leading to newborn mortality and developmental disabilities, imposing significant burdens on families, society, and healthcare systems. In recent years, Taiwan has also faced continuously declining birth rates, further emphasizing the importance of newborn health issues. Therefore, effectively predicting and reducing the occurrence of premature birth and low birth weight has become a key focus of current research. This study designed two-stage experiments, analyzing clinical data of pregnant women and newborns to identify potential factors leading to premature birth and low birth weight using machine learning methods. The first experiment focused on a predictive model for premature birth, while the second experiment focused on a predictive model for low birth weight. The results showed that extreme gradient boosting trees performed best in predicting premature birth and low birth weight in newborns. Based on the model results, a clinical decision support system for newborn health was established. This system serves as a tool for early identification of high-risk newborns, enabling healthcare professionals to intervene and provide care promptly, allocate medical resources more accurately, and reduce the occurrence of adverse health outcomes. | en_US |